The Financial Impact of ESG Factors and Market Dynamics on Service Sector Firm Performance: An Empirical Analysis Using Panel Data and Machine Learning

This study empirically investigates the financial impact of ESG factors and market dynamics on service sector firm excess returns using a 2012-2021 panel dataset of 14 firms.Panel regression and time series analysis, along with machine learning models, were employed.Findings indicate that traditional Fama-French factors significantly influence returns, while a higher aggregated ESG score unexpectedly correlates negatively with excess returns.Marketlevel ESG factors show dynamic interdependencies with market returns but no Granger causality.Machine learning models demonstrate high predictive accuracy for future return direction, with financial and ESG features as key predictors.These exploratory findings are useful for understanding complex ESG-financial relationships in the service sector, guiding nuanced corporate sustainability strategies and investment decisions, and contributing to the ongoing debate on ESG materiality.

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Publication Details

Journal
International Journal of Data Science and Big Data Analytics
Published
2025-05-25
DOI
https://doi.org/10.51483/ijdsbda.5.1.2025.21-48
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
0.00
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article

The Financial Impact of ESG Factors and Market Dynamics on Service Sector Firm Performance: An Empirical Analysis Using Panel Data and Machine Learning

Henry Efe Onomakpo Onomakpo
International Journal of Data Science and Big Data Analytics
Impact of AI and Big Data on Business and Society
article

The Financial Impact of ESG Factors and Market Dynamics on Service Sector Firm Performance: An Empirical Analysis Using Panel Data and Machine Learning

Henry Efe Onomakpo Onomakpo
article en

Abstract

This study empirically investigates the financial impact of ESG factors and market dynamics on service sector firm excess returns using a 2012-2021 panel dataset of 14 firms.Panel regression and time series analysis, along with machine learning models, were employed.Findings indicate that traditional Fama-French factors significantly influence returns, while a higher aggregated ESG score unexpectedly correlates negatively with excess returns.Marketlevel ESG factors show dynamic interdependencies with market returns but no Granger causality.Machine learning models demonstrate high predictive accuracy for future return direction, with financial and ESG features as key predictors.These exploratory findings are useful for understanding complex ESG-financial relationships in the service sector, guiding nuanced corporate sustainability strategies and investment decisions, and contributing to the ongoing debate on ESG materiality.

International Journal of Data Science and Big Data AnalyticsVol. 5(1)
Industry, innovation and infrastructure
Openalex Percentile: Top 79%
Impact of AI and Big Data on Business and Society
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The Financial Impact of ESG Factors and Market Dynamics on Service Sector Firm Performance: An Empirical Analysis Using Panel Data and Machine Learning — Henry Efe Onomakpo Onomakpo · International Journal of Data Science and Big Data Analytics (2025) | TGRS Research Map | TGRS